code: def __init__(self) -> None:
      super().__init__()
      self.tsr_steps: int = 24
      model = nn.Sequential(
          nn.Conv2d(
              2049, 1024, kernel_size=3, stride=1, padding=1, bias=False
          ),
          nn.BatchNorm2d(
              1024,
              eps=1e-05,
              momentum=0.1,
              affine=True,
              track_running_stats=True,
          ),
          nn.ReLU(inplace=True),
          Bottleneck(1024, 256),
          nn.PixelShuffle(2),
          Bottleneck(256, 64),
          nn.PixelShuffle(2),
          Bottleneck(64, 16),
          nn.PixelShuffle(2),
          nn.Conv2d(
              16, 16, kernel_size=3, stride=1, padding=1, bias=False
          ),
          nn.PixelShuffle(2),
          nn.Conv2d(
              4, 4, kernel_size=3, stride=1, padding=1, bias=True
          ),
          nn.ReLU(inplace=True),
          nn.Conv2d(
              4, 4, kernel_size=3, stride=1, padding=1, bias=True
          ),
      )
      self.net = model
      self.times = torch.linspace(0, 1, self.tsr_steps)
  
imports: from typing import Callable, Dict, List, Optional, Type
  import torch
  from torch import nn
  from kornia.core import Module, Tensor, concatenate, stack
  from kornia.utils.helpers import map_location_to_cpu
label_desc: Use image node_id 1 to create a new RenderingDeFMO object from inherited base classes: Module with example: obj = RenderingDeFMO()